1) Field of the Invention
The present invention relates to a technology for controlling a network traffic by improving an accuracy in predicting the network traffic and by optimizing a timing of a traffic control.
2) Description of the Related Art
In a conventional communication network, a prediction technology for future traffic based on past traffic (actual measurement value), such as a method of a linear-time-series analysis based on an autoregressive integrated moving average (ARIMA) model, has been suggested.
The linear-time-series analysis is used for analyzing a value (data) measured over time to perform a prediction or a verification of an assumption.
In the linear-time-series analysis, a measurement value at a certain point in time is represented by a linear polynomial of past measurement value and past noise components. Therefore, a value predicted by the linear-time-series analysis largely depends on the past measurement value.
In the ARIMA model, a time series zt is represented by a linear polynomial of autoregressive components zt-1, zt-2, . . . , and zt-p, and moving average components at, at-1, . . . , and at-g of white noise. The model is generally expressed asφ(B)Φ(BS)ΔSDΔdzt=θ(B)Θ(Bs)at where
zt: Deviation from the time series average,
at: White noise (error),
B: Lug operator (Bzt=zt-1),
Δ: Difference operator (Δzt=zt−zt-1),
ΔS: Seasonal difference operator (Δszt=zt−zt-s),
φ(B)=1−φ1B−φ2B2− . . . −-φpBp (Autoregressive components),
Φ(BS)=1−Φ1Bs−Φ2B2s− . . . −ΦPBPs (Periodic autoregressive components),
θ(B)=1−θ1B−θ2B2− . . . −θqBq (Moving average components of white noise), and
Θ(Bs)=1−Θ1Bs−Θ2B2s− . . . −ΘQBQs (Periodic moving average components of white noise).
FIG. 25 is a diagram for explaining conventional traffic prediction using the ARIMA model. In FIG. 25, at step SZ1, information on traffic measured in the target network (that is, data amount passing through the network) is input.
At step SZ2, ARIMA model identification processing is executed. That is, after an autocorrelation function (ACF) and a partial autocorrelation function (PACF) in the input information are calculated, the ARIMA model (p, d, q, P, D, Q, s) is identified by the shapes thereof.
At step SZ3, parameter presumption processing is executed. That is, the parameters of the ARIMA model (φ1 . . . φp, θ1 . . . θq, Φ1 . . . Φp, Θ1 . . . ΘQ) are presumed by the method of maximum likelihood or the method of least squares.
At step SZ4, the accuracy of the ARIMA model identified at step SZ2 and the parameters presumed at step SZ3 are determined. At step SZ5, future date and time are applied to the ARIMA model to predict the future traffic.
By the way, in the conventional network, traffic control for decreasing the traffic is performed before the traffic reaches the maximum capacity.
A preparation time is required since the traffic control is started until the effect of the control is seen. Conventionally, therefore, a threshold is set to a predetermined percentage (for example, 80%) of the maximum capacity to start the traffic control when the traffic reaches the threshold.
Conventionally, as shown in FIG. 26, when the traffic control such as a discard control (control of discarding a low-preference traffic) and a path route switching control is executed, the traffic in the affected link largely fluctuates before and after the traffic control (in the example shown in FIG. 26, the traffic decreases suddenly, as shown by solid line, due to the execution of the traffic control).
Therefore, when the traffic is predicted by the conventional ARIMA model, a sudden change appears in the predicted traffic in the prediction period (as shown in FIG. 26 by a broken-line) since the traffic in the data collection period for prediction is fluctuated due to the traffic control.
Thus, the traffic control adversely affects the predicted traffic, thereby decreasing the accuracy in predicting the traffic.
On the other hand, in the conventional traffic control in which a threshold to a certain percentage of the maximum capacity is set, it is difficult to ascertain to which percentage the threshold is to be set. Further, in the conventional traffic control in which the traffic control is executed when the traffic exceeds the threshold, control may be too early, too late to cause an excess of the maximum capacity, or unnecessary control may be executed.